Genome-Wide Association Analysis of Bladder Cancer

膀胱癌的全基因组关联分析

基本信息

批准号：
7935041
负责人：
Xifeng Wu
金额：
$ 30.58万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-01 至 2011-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7935041
关键词：
Adopted Age Algorithms Architecture Arts Biological Biological Assay Bladder Cancer Center Cancer Etiology Case Series Case-Control Studies Cell Cycle Regulation Chemoprevention Chemopreventive Agent Clinic Clinical Complex Computational algorithm Custom Cytogenetics DNA DNA Repair Data Databases Dietary Practices Disease Doctor of Medicine Environment Environmental Exposure Environmental Risk Factor Epidemiology Ethnic Origin Frequencies Funding Future Gender Genes Genetic Genetic Polymorphism Genetic Risk Genetic Variation Genomics Genotype Goals Haplotypes Health Benefit Human Genome Individual Individual Differences Inherited International Intervention Joints Length Light Location Lymphocyte Machine Learning Malignant neoplasm of urinary bladder Maps Medicine Modeling Molecular Mutagens New England New Hampshire Newly Diagnosed Occupational Exposure One-Step dentin bonding system Outcome Pattern Penetrance Phenotype Physicians Plasma Cells Play Polymorphism Analysis Population Predisposition Public Health Recruitment Activity Reproduction spores Resources Review Literature Risk Role Sampling Screening procedure Serum Single Nucleotide Polymorphism Smoking Specimen Staging Statistical Methods Subgroup Suspension substance Suspensions TNFRSF5 gene Technology University of Texas M D Anderson Cancer Center Validation Variant aggregation pathway base cancer risk carcinogenesis case control college computer based statistical methods cost density design epidemiologic data follow-up gene interaction genome wide association study genome-wide high risk medical specialties novel parent grant public health relevance repository telomere tool

项目摘要

DESCRIPTION (provided by applicant): This proposal builds on a rich resource of bladder cancer (BC) cases and controls derived from two ongoing BC studies at the University of Texas M.D. Anderson Cancer Center, and from two large independent U.S. BC studies - the New England BC Study and the New Hampshire BC Study. The goal is to identify genetic loci that predispose individuals to BC through a genome-wide scanning approach. There are five specific aims. Aim 1 is to first perform genome-wide, high-density SNP genotyping using the Illumina HumanHap550 SNP platform on 800 cases and 800 controls from M.D. Anderson, with a target of candidate SNPs of about 28,000; followed by an internal validation to narrow down candidate SNPs to about 1,536 using additional 800 pairs of cases and controls. Illumina's Custom Infinium array will be the genotyping format. In this aim, in addition to individual SNP analysis, we will also implement haplotype-based analyses and pathway aggregation analysis to identify additional genetic loci that might have been overlooked using individual SNP analysis. Aim 2 is the first external validation of the 1,536 SNPs from Aim 1 using 1,000 pairs of cases and controls from the New England BC Study. Illumina's GoldenGate assay will be the genotyping format. After this stage, the candidate SNPs will be narrowed down to about 100. Aim 3 is the second independent external validation using 750 pairs of cases and controls form the New Hampshire BC Study. In this aim, the 100 top candidate SNPs passed from Aims 1 and 2 plus additional functional SNPs in genes containing these SNPs will be genotyped. GoldenGate assay will be used for this aim. Aim 4 is to perform fine mapping studies in the flanking regions of each of the top 25 SNP loci confirmed in Aim 3 to identify causative loci. This will utilize all 6,700 cases and controls. An average of 15 additional SNPs (tagging SNPs and functional SNPs) per gene is expected. Aim 5 is to apply novel machine-learning tools to identify any gene-environment and gene-gene interactions greatly influencing risk for BC in all the 6700 subjects. These analyses will be utilized to examine SNP main effect and develop and validate algorithms that will identify individuals at highest risk for BC, given their personal exposure patterns and their genetic risk profiles. This proposal applies state of art technology to perform a multistage, genome-wide SNP analysis in three largest, well-characterized U.S. population of BC cases and controls, and incorporates complete epidemiologic data and rich and unique functional data. In addition, results from this study will be provided to the International Consortium of BC Case Control Studies for future validation. The ability to identify genetically susceptible, high-risk subgroups that would benefit from intensive screening and/or chemopreventive interventions is of immense clinical and public health benefit. PUBLIC HEALTH RELEVANCE Bladder cancer (BC) is a disease mainly caused by smoking and occupational exposure. However, only a small percentage of exposed individuals develop BC. Inherited host genetic factors may play an important role in determining an individual's susceptibility to BC. This proposal builds on three largest well-characterized U.S. populations of BC cases and controls - the M. D. Anderson BC study, the New England BC Study and the New Hampshire BC Study. A total of 6700 cases and controls will be used. The goal is to identify genetic loci that predispose individuals to BC through a non-biased, discovery- driven, genome-wide scanning approach and to incorporate complete epidemiologic data and rich and unique functional data. About 550,000 genetic variations of human genome will be initially screened in an M.D. Anderson population consisting of an equal number of BC cases and normal controls. About 28,000 top candidate variations that are potentially associated with increased BC risk will be first narrowed down and internally replicated in a second M.D. Anderson population, and then be validated in the other two independent U.S. BC populations. Finally, the causative genetic loci that predispose individuals to BC will be mapped. This study is significant because by identifying BC susceptibility loci, it will shed light into the biological mechanisms of BC etiology. Furthermore, it may facilitate identifying high-risk subgroups of individuals for BC, given their genetic makeup and environmental exposures. The ability to identify high-risk subgroups of individuals for BC will provide immense public health benefit for those high- risk people who may be subjected to close surveillance and chemoprevention.

描述（由申请人提供）：该提案建立在丰富的膀胱癌 (BC) 病例和对照资源的基础上，这些资源来自德克萨斯大学 M.D. 安德森癌症中心正在进行的两项 BC 研究，以及两项大型独立的美国 BC 研究 - 新英格兰 BC 研究和新罕布什尔州BC 研究。目标是通过全基因组扫描方法识别导致个体易患 BC 的遗传位点。有五个具体目标。目标 1 是首先使用 Illumina HumanHap550 SNP 平台对来自 M.D. Anderson 的 800 个病例和 800 个对照进行全基因组、高密度 SNP 基因分型，目标候选 SNP 约为 28,000 个；随后使用额外的 800 对病例和对照进行内部验证，将候选 SNP 范围缩小到约 1,536 个。 Illumina 的 Custom Infinium 阵列将成为基因分型格式。为此，除了个体 SNP 分析之外，我们还将实施基于单倍型的分析和通路聚合分析，以识别使用个体 SNP 分析可能被忽略的其他遗传位点。目标 2 是使用来自新英格兰 BC 研究的 1,000 对病例和对照对目标 1 中的 1,536 个 SNP 进行的首次外部验证。 Illumina 的 GoldenGate 检测将成为基因分型格式。此阶段之后，候选 SNP 将缩小至约 100 个。目标 3 是使用新罕布什尔州 BC 研究中的 750 对病例和对照进行的第二次独立外部验证。在此目标中，将对目标 1 和 2 传递的 100 个顶级候选 SNP 以及包含这些 SNP 的基因中的其他功能性 SNP 进行基因分型。 GoldenGate 检测将用于此目的。目标 4 是在目标 3 中确认的前 25 个 SNP 位点的侧翼区域进行精细定位研究，以识别致病位点。这将利用全部 6,700 个案例和对照。预计每个基因平均有 15 个额外的 SNP（标记 SNP 和功能性 SNP）。目标 5 是应用新颖的机器学习工具来识别对所有 6700 名受试者的 BC 风险有重大影响的任何基因-环境和基因-基因相互作用。这些分析将用于检查 SNP 主要效应，并开发和验证算法，根据个人暴露模式和遗传风险状况，识别 BC 风险最高的个体。该提案应用最先进的技术对美国三个最大的、特征明确的 BC 病例和对照人群进行多阶段、全基因组 SNP 分析，并纳入完整的流行病学数据和丰富且独特的功能数据。此外，这项研究的结果将提供给不列颠哥伦比亚省病例对照研究国际联盟以供未来验证。识别遗传易感性、高风险亚群的能力将受益于强化筛查和/或化学预防干预措施，这具有巨大的临床和公共卫生益处。公共卫生相关性膀胱癌（BC）是一种主要由吸烟和职业暴露引起的疾病。然而，只有一小部分接触过的人会患上 BC。遗传的宿主遗传因素可能在决定个体对 BC 的易感性方面发挥重要作用。该提案建立在美国三个最大的 BC 病例和对照群体的基础上——MD Anderson BC 研究、新英格兰 BC 研究和新罕布什尔 BC 研究。总共将使用 6700 个病例和对照。目标是通过无偏见、发现驱动的全基因组扫描方法来识别使个体易患 BC 的遗传位点，并整合完整的流行病学数据和丰富且独特的功能数据。人类基因组的约 550,000 个遗传变异将在 M.D. Anderson 人群中进行初步筛选，该人群由同等数量的 BC 病例和正常对照组成。大约 28,000 个可能与 BC 风险增加相关的顶级候选变异将首先缩小范围，并在第二个 M.D. Anderson 人群中进行内部复制，然后在其他两个独立的美国 BC 人群中进行验证。最后，将绘制导致个体易患 BC 的致病基因位点。这项研究意义重大，因为通过鉴定 BC 易感位点，它将揭示 BC 病因的生物学机制。此外，考虑到其基因构成和环境暴露，它可能有助于识别 BC 的高风险亚组。识别 BC 高风险亚群的能力将为那些可能受到密切监测和化学预防的高风险人群提供巨大的公共卫生益处。